A 3.8-μW 10-Keyword Noise-Robust Keyword Spotting Processor Using Symmetric Compressed Ternary-Weight Neural Networks

Abstract

A ternary-weight neural network (TWN) inspired keyword spotting (KWS) processor is proposed to support complicated and variable application scenarios. To achieve high-precision recognition of 10 keywords under 5dB∼Clean wide range of background noises, a convolution neural network consists of 4 convolution layers and 4 fully connected layers, with modified sparsity-controllable Truncated Gaussian Approximation based ternary-weight training is used. End to end optimization composed of three techniques are utilized: 1) the stage-by-stage bit-width selection algorithm to optimize the hardware overhead of FFT; 2) the lossy compressed TWN with symmetric kernel training (SKT) and dedicated internal data reuse computation flow; 3) the error inter-compensation approximate addition tree to reduce the computation overhead with marginal accuracy loss. Fabricated in an industrial 22nm CMOS process, the processor realizes up to 10 keywords real-time recognition under 11 background noise types, with the accuracy of 90.6%@clean and 85.4%@5dB. It consumes an average power of 3.8 µW at 250KHz and the normalized energy efficiency is 2.79× higher than state-of-the-art.